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Faults detection and classification in a centrifugal pump from vibration data using markov parameters

机译：来自使用马尔可夫参数的振动数据的离心泵故障检测和分类

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One of the strategies to detect and classify faults in mechanical systems is to use a time domain family of techniques known as output-only methods. Those methods are based on the analysis of sample covariance matrices, which are estimated from vibration data extracted from mechanical systems under unmeasured natural excitation. Using the stochastic realization theory, it is possible to derive Markov parameters from sample covariance matrices. Those parameters contain only the significant spectral components from data. In this paper, a novel output-only method based on the Markov parameters is proposed to diagnose faults. The idea is to use the Markov parameters estimated from vibration data as features in classification algorithms based on convex optimization. The method was applied to diagnose incipient cavitation failures in a water supply network centrifugal pump. A low-cost triaxial vibration sensor developed by one of the authors was used to register the vibration data. The proposed method was compared to the analysis based on sample covariance matrices demonstrating the advantages related to the use of the Markov parameters.
机译：检测和分类机械系统故障的策略之一是使用称为仅输出方法的时域系列技术。这些方法基于对样品协方差矩阵的分析，这些矩阵从未在未测量的自然激励下从机械系统提取的振动数据估计。使用随机的实现理论，可以从样本协方差矩阵派生马尔可夫参数。这些参数仅包含来自数据的重要频谱分量。本文提出了一种基于马尔可夫参数的新型输出方法来诊断故障。该想法是根据凸优化使用从振动数据估计的Markov参数作为分类算法中的特征。应用该方法以诊断供水网络离心泵中的初生空化故障。由其中一位作者开发的低成本三轴振动传感器用于注册振动数据。将所提出的方法与基于样本协方差矩阵的分析进行了比较，证明了与Markov参数的使用相关的优点。

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